
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


data=pd.read_csv("11-4-Dataset-Predicting Placement in Campus Recruitment.csv")


data.head()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 215 entries, 0 to 214
Data columns (total 15 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   sl_no           215 non-null    int64  
 1   gender          215 non-null    object 
 2   ssc_p           215 non-null    float64
 3   ssc_b           215 non-null    object 
 4   hsc_p           215 non-null    float64
 5   hsc_b           215 non-null    object 
 6   hsc_s           215 non-null    object 
 7   degree_p        215 non-null    float64
 8   degree_t        215 non-null    object 
 9   workex          215 non-null    object 
 10  etest_p         215 non-null    float64
 11  specialisation  215 non-null    object 
 12  mba_p           215 non-null    float64
 13  status          215 non-null    object 
 14  salary          148 non-null    float64
dtypes: float64(6), int64(1), object(8)
memory usage: 25.3+ KB


data.isnull().sum()

sl_no              0
gender             0
ssc_p              0
ssc_b              0
hsc_p              0
hsc_b              0
hsc_s              0
degree_p           0
degree_t           0
workex             0
etest_p            0
specialisation     0
mba_p              0
status             0
salary            67
dtype: int64


print("General Specifications about Data using Count Plot")


plt.figure(figsize = (15,7))
plt.subplot(231)
ax = sns.countplot(x = 'gender', data = data)

plt.figure(figsize = (15,7))
plt.subplot(232)
ax = sns.countplot(x = 'hsc_s', data = data)

plt.figure(figsize = (15,7))
plt.subplot(233)
ax = sns.countplot(x = 'degree_t', data = data)

plt.figure(figsize = (15,7))
plt.subplot(234)
ax = sns.countplot(x = 'specialisation', data = data)

plt.figure(figsize = (15,7))
plt.subplot(235)
ax = sns.countplot(x = 'workex', data = data)

plt.figure(figsize = (15,7))
plt.subplot(236)
ax = sns.countplot(x = 'status', data = data)

General Specifications about Data using Count Plot


placed = data[data.salary != 0]
print("Graph to show Salary Distribution: ")
sns.displot(placed['salary'])

Graph to show Salary Distribution:

<seaborn.axisgrid.FacetGrid at 0x2819ae34550>


import plotly_express as px
print("Distribution of Male and Female across different Specialisations and Salary")
px.violin(placed, y = 'salary', x = 'specialisation', color = 'gender', box = True, points = 'all')

Distribution of Male and Female across different Specialisations and Salary


data.drop(['salary','ssc_b','hsc_b','sl_no'],inplace=True, axis=1)


data.head()


from sklearn.preprocessing import LabelEncoder
le_gender = LabelEncoder()
le_hscs = LabelEncoder()
le_degreet = LabelEncoder()
le_workex = LabelEncoder()
le_specialisation = LabelEncoder()
le_status = LabelEncoder()

data['gender'] = le_gender.fit_transform(data['gender'])
data['degree_t'] = le_degreet.fit_transform(data['degree_t'])
data['workex'] = le_workex.fit_transform(data['workex'])
data['specialisation'] = le_specialisation.fit_transform(data['specialisation'])
data['status'] = le_status.fit_transform(data['status'])
data['hsc_s'] = le_hscs.fit_transform(data['hsc_s'])


X=data.drop('status',axis=1)
y=data['status']
print(X)
print(y)

     gender  ssc_p  hsc_p  hsc_s  degree_p  degree_t  workex  etest_p  \
0         1  67.00  91.00      1     58.00         2       0     55.0   
1         1  79.33  78.33      2     77.48         2       1     86.5   
2         1  65.00  68.00      0     64.00         0       0     75.0   
3         1  56.00  52.00      2     52.00         2       0     66.0   
4         1  85.80  73.60      1     73.30         0       0     96.8   
..      ...    ...    ...    ...       ...       ...     ...      ...   
210       1  80.60  82.00      1     77.60         0       0     91.0   
211       1  58.00  60.00      2     72.00         2       0     74.0   
212       1  67.00  67.00      1     73.00         0       1     59.0   
213       0  74.00  66.00      1     58.00         0       0     70.0   
214       1  62.00  58.00      2     53.00         0       0     89.0   

     specialisation  mba_p  
0                 1  58.80  
1                 0  66.28  
2                 0  57.80  
3                 1  59.43  
4                 0  55.50  
..              ...    ...  
210               0  74.49  
211               0  53.62  
212               0  69.72  
213               1  60.23  
214               1  60.22  

[215 rows x 10 columns]
0      1
1      1
2      1
3      0
4      1
      ..
210    1
211    1
212    1
213    1
214    0
Name: status, Length: 215, dtype: int32


X.head()


y.head()

0    1
1    1
2    1
3    0
4    1
Name: status, dtype: int32


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=1)


data.describe()


from sklearn.ensemble import RandomForestClassifier

model=RandomForestClassifier(n_estimators=50, criterion='entropy', random_state=1)
model.fit(X_train,y_train)

RandomForestClassifier(criterion='entropy', n_estimators=50, random_state=1)


predictions=model.predict(X_test)
predictions

array([1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,
       1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1])


from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, classification_report, mean_absolute_error


print('Accuracy score:', accuracy_score(y_test,predictions))

Accuracy score: 0.8837209302325582


print('confusion_matrix:\n' )
cm = confusion_matrix(y_test,predictions)
disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=model.classes_)
disp.plot()

confusion_matrix:

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2819d596700>


print('classification report:\n', classification_report(y_test,predictions))

classification report:
               precision    recall  f1-score   support

           0       0.92      0.73      0.81        15
           1       0.87      0.96      0.92        28

    accuracy                           0.88        43
   macro avg       0.89      0.85      0.87        43
weighted avg       0.89      0.88      0.88        43


print('mean absolute error:\n', mean_absolute_error(y_test,predictions))

mean absolute error:
 0.11627906976744186


#random forest=0.88
#svm=0.86
#logistic regression = 0.86
#decision tree=0.76
# k neighbors=0.81

	gender	ssc_p	hsc_p	hsc_s	degree_p	degree_t	workex	etest_p	specialisation	mba_p	status
count	215.000000	215.000000	215.000000	215.000000	215.000000	215.000000	215.000000	215.000000	215.000000	215.000000	215.000000
mean	0.646512	67.303395	66.333163	1.372093	66.370186	0.600000	0.344186	72.100558	0.441860	62.278186	0.688372
std	0.479168	10.827205	10.897509	0.580978	7.358743	0.890238	0.476211	13.275956	0.497767	5.833385	0.464240
min	0.000000	40.890000	37.000000	0.000000	50.000000	0.000000	0.000000	50.000000	0.000000	51.210000	0.000000
25%	0.000000	60.600000	60.900000	1.000000	61.000000	0.000000	0.000000	60.000000	0.000000	57.945000	0.000000
50%	1.000000	67.000000	65.000000	1.000000	66.000000	0.000000	0.000000	71.000000	0.000000	62.000000	1.000000
75%	1.000000	75.700000	73.000000	2.000000	72.000000	2.000000	1.000000	83.500000	1.000000	66.255000	1.000000
max	1.000000	89.400000	97.700000	2.000000	91.000000	2.000000	1.000000	98.000000	1.000000	77.890000	1.000000

Importing Libraries¶

Reading and Analysing the Data¶

Data Visualization¶

Dropping unnecessary Columns¶

Labeling the Data¶

Splitting the Data into 2 sets for predictions¶

i.e. one df for input and other as predictions¶

Train-Test-Split¶

Model Seletion and Training¶

Predictions¶

Evalution of the Model¶

	sl_no	gender	ssc_p	ssc_b	hsc_p	hsc_b	hsc_s	degree_p	degree_t	workex	etest_p	specialisation	mba_p	status	salary
0	1	M	67.00	Others	91.00	Others	Commerce	58.00	Sci&Tech	No	55.0	Mkt&HR	58.80	Placed	270000.0
1	2	M	79.33	Central	78.33	Others	Science	77.48	Sci&Tech	Yes	86.5	Mkt&Fin	66.28	Placed	200000.0
2	3	M	65.00	Central	68.00	Central	Arts	64.00	Comm&Mgmt	No	75.0	Mkt&Fin	57.80	Placed	250000.0
3	4	M	56.00	Central	52.00	Central	Science	52.00	Sci&Tech	No	66.0	Mkt&HR	59.43	Not Placed	NaN
4	5	M	85.80	Central	73.60	Central	Commerce	73.30	Comm&Mgmt	No	96.8	Mkt&Fin	55.50	Placed	425000.0